Heuristic method of automated and learning control, and building automation systems thereof

ABSTRACT

Disclosed is a method of physical-model based building automation using in-situ regression to optimize control systems.

FIELD

The present disclosure relates to control of building systems using automated means. More specifically the present disclosure relates to a model-based building automation system wherein a method of heuristic tuning or regression fitting is utilized with live building data to automatically improve the system model. The present disclosure particularly addresses the control and automation of HVAC, energy, lighting, and/or irrigation systems.

BACKGROUND

Building automation systems are used in buildings to manage energy systems, HVAC systems, irrigation systems, accessory building systems, controllable building structures, and the like.

There has been little effort toward incorporating these systems into a controller with a unified operational model, thus allowing a more intelligent means of managing the energy interrelationships between various building components and their respective control algorithms. This is due, in part, to the fact that the field has been dominated by model-free control loops, which have difficulty managing sophisticated, tightly-coupled systems or adaptively tuning complex models in a predictable manner.

There have been studies exploring the concept of automated commissioning, however the methods used to date have typically required an occupancy-free training period, during which the building is subjected to an artificial test regime, which limits the potential for retro-commissioning or continuous commissioning. More importantly, the work to date has been limited to simple HVAC systems having topologies known a priori, and lacks the ability to scale to complex ad hoc arrangements that represent the diversity of building topologies. In addition, the existing approaches lack a method of combined commissioning of non-HVAC or climate-adaptive energy interactive building components.

Efforts towards closed-loop control system auto-commissioning and optimization have been limited. Most efforts in the area of auto-commissioning have focused on a specific problem set, for example VAV commissioning, or air handler commissioning. The majority of the efforts to date have focused on manual commissioning through user analysis of building automation system data, user-driven computer tools for management of the commissioning process, commissioning test routines, or fault detection.

Recently, the most common approach in the industry has been to focus on building and energy monitoring and analytics with the intent of providing an energy “dashboard” for the building. The most sophisticated examples of dashboards provide statistical based diagnostics of equipment behavior changes, failures, or the like. This “outside-the-box-looking-in” approach can provide information, but relies on the administrator to understand the problem and close the loop, both of which are rare occurrences.

Efforts to date have used physical models as a reference, and benchmark the reference against the actual building using data mining to create control strategies. This requires a person in the loop, and thus limits applicability to projects with means for a highly skilled engineering team. It further requires buildings to be tested off-line, which is rarely acceptable.

Almost all building controls today are model-free. The model-free approach, while simple to implement, becomes quite difficult to manage and optimize as the complexity of the system increases. It also lacks the inherent self-knowledge to provide new approaches to programming, such as model-driven graphical programming, or govern the interconnections between components and sub-system synergistics.

Physical model based approaches to date have been limited in scope and specific to known models defined a-priori. They have thus lacked the ability to enable users to create n-complex systems of interconnected sub-systems by ad hoc means, use simple graphical user interfaces to define a system, or enable system model to evolve their control optimization and commissioning over time in situ.

SUMMARY

The present disclosure applies a closed loop, heuristically tuned, model-based control algorithm to building automation.

There are several advantages that can be gained from applying model-based control to building automation systems.

Model based control allows for altering control schemes based on external factors including but not limited to weather_(;) occupancy, and user input. The physical system model allows for simulation of these external factors' effects on building comfort and efficiency. Once the effects of said factors on the system are known the controller can take the necessary control actions to compensate for the effects. For example, in some embodiments, a building with higher occupancy will require less heating or more cooling to offset the heat generated by the occupants.

Model based control allows for inclusion of predicted external factors. For example, in some embodiments, future weather predictions can be taken into account when deciding on control actions. This would allow the controller to more effectively utilize resources by building up energy stores while it is cheap and depleting stores when resources are expensive. In some embodiments, future occupancy predictions may be included in the model-based control system. A schedule may be monitored and comfort settings may be allowed to be changed to prioritize other factors like energy efficiency during times no occupancy is expected.

Model based control enables the system controller to consider any and all external and/or intrinsic influencing factors from all periods of time, current, past or future. This enables what may be called “future-forward control”. This is the generation of a sequence or control regime prior to the application of the control regime.

Model based control allows for less complicated commissioning. The controller can perform the abstraction of the system into mathematical models. Removing this level of abstraction from the user allows for faster, easier, more accurate, and more flexible model creation.

Model based control also provides system labeling through a means of ontology. Because the purpose, behavior, and semantics of that behavior are known statically, the system can interpret the meaning of equipment or object behavior during runtime. Regressions of equipment models within the scope of their known ontologies enable adaptively fitted models in situ, and also detect faults as models lose compliance with their fit.

Model based control allows for real time system monitoring and software repair. By including a model of all of the equipment in the system, and sensing equipment performance, the controller may sense equipment faults when there is a significant divergence between system simulation outputs and sensor data. Because the controller has a model of the equipment's, sub-system's, or system's predicted operation, any deviation from normal operation may be investigated automatically. The knowledge of a fault allows for diagnostic, remediation, and/or alerting actions to be taken by the controller. The remediation of soft equipment faults is beneficial as it replaces the need for human interaction with malfunctioning equipment.

Model based control allows for changing control actions in response to time varying parameters. These parameters include but are not limited to equipment aging, weather, occupancy, equipment fault, insulation values, resource costs, and/or user input. Each of these parameters will affect control path calculations.

All of the benefits of model-based control systems may be extended by employing heuristic techniques which adjust based on past regressions. By allowing the model to be tuned by heuristics based on past regressions, the model can compensate for inaccuracies in the originally included data. The model can also interpolate unknown values to facilitate more complete system optimization.

While model based control can require increased user handling of parameters to fit a model to a controlled application, the present disclosure describes a methodology by which real-time regressions of stored “future-forward” control predicted sequences are compared to the actual time series of events as resulting from that control. This methodology can allow model based control to automate a system, then constantly learn from the system to tune its parameters so that only limited data is required to set up a system. This disclosure describes the method by which a fitted system can detect the system falling out of compliance through the same regression methodology.

DESCRIPTION OF THE DRAWINGS

To further clarify various aspects of some example embodiments of the present disclosure, a more particular description of the disclosure will be rendered by reference to specific embodiments thereof that are illustrated in the appended drawings. It is appreciated that the drawings depict only illustrated embodiments of the disclosure and are therefore not to be considered limiting of its scope. The disclosure will be described and explained with additional specificity and detail through the use of the accompanying drawings in which:

FIG. 1—Physical model system block diagram

FIG. 2—Historical and predicted sensor values and time-series thereof

FIG. 3—Heuristic or regression fitting method

FIG. 4—Architectural embodiment of an example of a simple controlled system

FIG. 5—Architectural embodiment of a building control loop

FIG. 6—Architectural embodiment of a building control loop containing a simulation engine wherein the simulation engine contains a physical model

FIG. 7—Architectural embodiment of a building control loop containing a simulation engine and cost function wherein the simulation engine contains a physical model.

FIG. 8—Architectural embodiment of a building control loop containing a simulation engine and cost function wherein the simulation engine contains a physical model, which is tuned, based on past regressions

FIG. 9—Selection of optimal control regime from candidate control regimes

DESCRIPTION

The embodiments of the present disclosure described herein are not intended to be exhaustive or to limit the disclosure to the precise forms disclosed in the following detailed description. Rather, the embodiments are chosen and described so that others skilled in the art may appreciate and understand the principles and practices of the present disclosure.

The following embodiments and the accompanying drawings, which are incorporated into and form part of this disclosure, illustrate embodiments of the disclosure and together with the description, serve to explain the principles of the disclosure. To the accomplishment of the foregoing and related ends, certain illustrative aspects of the disclosure are described herein in connection with the following description and the annexed drawings. These aspects are indicative, however of, but a few of the various ways in which the principles of the disclosure can be employed and the subject disclosure is intended to include all such aspects and their equivalents. Other advantages and novel features of the disclosure will become apparent from the following detailed description of the disclosure when considered in conjunction with the drawings.

Explanation will be made below with reference to the figures referenced above for illustrative embodiments concerning the predictive building control loop according to the current disclosure.

A building control system contains a control loop such as illustrated in FIG. 5. The control loop contains a controller that makes decisions based on sensor data or some other feedback mechanism. The control decisions are then applied to the controlled system. The controller may be comprised of systems including but not limited to software, hardware, mechanical, and/or cloud based systems. The resulting effects on the system are monitored by the feedback mechanism. An example of a building control loop is: the sensor data is comprised of an air temperature sensor, the controller is comprised of a thermostat, and the controlled system is comprised of a furnace, fan, air conditioner, and building. In this case the furnace and air conditioner are sources, the fan is a transport, and the building is a sink. The controlled system can be represented as in FIG. 1; a system comprised of sources, sinks, and transports, possibly with other intermediate components.

Another embodiment of a controlled system is shown in FIG. 4. In FIG. 4 the controlled system is comprised of a heat exchanger acting as a source, a pump as a transport, and a storage tank as a sink.

One embodiment in FIG. 2 shows how multiple sensors may simultaneously feed data back to the controller in a time series. This time series data may then be extended into the future by outputs of the simulation engine.

The simulation engine output may be compared with the actual sensor data as shown in FIG. 3. By using a heuristic tuning method any difference between the simulation engine output and the sensor data can be used to tune the physical model parameters to better represent the controlled system. By constantly optimizing the model, any uncertainty or inaccuracy in the model(s) can be rectified.

FIG. 6 shows a controller containing a simulation engine. The simulation engine in FIG. 6 may allow the building system controller to predict the outcome of any available control action using its physical model of the system. Said predictions have many benefits, some of which are detailed below.

The physical model is defined as any model of the controlled system. The physical model may be time variant. One form of time variance that may be included in the physical model is comprised of heuristics. By employing heuristics, any control action may be evaluated, based on feedback from sensor data or some other form of feedback, to evaluate whether the control action had the intended effect. If the control action did not have the intended effect, the physical model may be changed to exert more effective control actions in the future.

FIG. 7 shows how a cost function may be applied to the simulation engine. Any and all resources may be given values in the cost function. Said resources include but are not limited to: natural gas, gasoline, propane, home heating oil, coal, water, electricity, emissions, equipment longevity, heat, and/or time outside of a defined comfort zone. Any possible control actions may be assessed according to the cost function in order to discover the optimal control action according to the cost function. The cost function may be time variant. The cost function may be linked with factors including, but not limited to, monetary value of said resources, user preferences, and/or changes in the physical model.

FIG. 8 shows how heuristics may be included in a model-based building control system. Employing heuristics with the physical model allows the model to be adaptive to issues such as time varying system elements, and/or inaccurate or incomplete starting datasets. By storing system reactions to past control actions, the physical model may be improved. Heuristics may be implemented by comparing the data stream of the controlled system to the output of the physical model-based simulation, as they respond to the same control stimuli. Any difference may be corrected by changing physical model inputs to influence the physical model outputs to match the data stream of the real world system. The use of a heuristic or regression algorithm to tune physical model parameters allows for substantial increases in system optimization, efficiency, and stability.

FIG. 9 shows how candidate control schemes, comprising a collection of control actions and corresponding valuation of the control actions, may be evaluated and compared, thus allowing for selection of the optimal control scheme among the candidates.

Although the disclosure has been explained in relation to certain embodiments, it is to be understood that many other possible modifications and variations can be made without departing from the spirit and scope of the disclosure. 

What is claimed is:
 1. A building system controller comprising: a controlled system; a physical model, comprised of a mathematical model of the controlled system; a simulation engine; a heuristic; a data stream, comprised of data from said controlled system; and a training loop; wherein the simulation engine can simulate the behavior of the controlled system by means of evaluating the physical model; and whereby said training loop compares the output of the simulation engine to the data stream using the heuristic, such that the physical model is regressed in a manner that the output of the simulation engine approaches the data stream.
 2. The building system controller of claim 1, wherein the regressed physical model is utilized by the simulation engine to more accurately predict the future behavior of a control loop, such that an optimal control regime may be more accurately computed.
 3. The building system controller of claim I, wherein the regressed physical model is utilized to detect faults, by detecting divergent parameters through regression of a previously regressed model.
 4. The building system controller of claim 1, wherein the building system controller also comprises a control loop that controls the controlled system.
 5. The building system controller of claim 1, wherein the building system controller also comprises a control loop that controls the controlled system using the output of the simulation engine to predict the future behavior of the controlled system under an arbitrary control regime.
 6. The building system controller of claim 2., wherein the control loop may apply the control regime to the controlled system and observe the response of the controlled system via the data stream, thereby completing the control loop.
 7. The building system controller of claim 5, wherein the control loop utilizes a cost function of the computed physical model to evaluate the cost of a given control regime.
 8. The building system controller of claim 7, wherein the cost may be evaluated in terms comprising one or more of, but not limited to: energy use, primary energy use, energy monetary cost, human comfort, the safety of building or building contents, the durability of building or building contents, microorganism growth potential, system equipment durability, system equipment longevity, environmental impact, and/or energy use CO2 potential.
 9. The building system controller of claim 2, wherein the optimal control regime minimizes a cost function such that the control loop may control the controlled system with least expense, according to the cost function.
 10. The building system controller of claim 2, wherein the optimal control regime may be selected through comparison of one or more potential control regimes, wherein the comparison is performed by means of one or more of the following but not limited to: differential comparison, multivariate population selection, statistical classification, clustering, feature extraction, preference ranking, and/or benchmarking.
 11. The building system controller of claim 1, wherein the controlled system may comprise one or more of the following: building automation systems, heating systems, cooling systems, ventilation systems, power management systems, renewable energy systems, irrigation systems, occupancy systems, lighting systems, environmental control systems, humidity control systems, air quality management systems, window operators, and/or shade systems.
 12. The building system controller of claim 1, wherein the regression comprises one or more of the following, but not limited to: differential comparison, multivariate population selection, statistical classification, clustering, feature extraction, preference ranking, and/or benchmarking.
 13. The building system controller of claim 1, wherein the regression is performed in a manner that the output of the simulation engine approaches the data stream, wherein the extent to which the simulation engine output approaches the data stream may mean, but is not limited to: a convergence of predicted and observed values; a reduction in error between predicted and observed values to within an arbitrary threshold; a reduction in uncertainty of predicted values to within an arbitrary threshold; reaching an arbitrary threshold on number of erroneous predictions; reaching an arbitrary threshold on number of accurate predictions; reaching an arbitrary threshold on number of data points processed; reaching an arbitrary threshold on computational time spent processing data.
 14. The building system controller of claim 1, wherein the regression evaluation method may comprise one or more of, but is not limited to: data slicing time slicing, time windowing, time batching, parameter slicing, parameter windowing, single-point, and/or multi-point.
 15. The building system controller of claim 1, wherein the heuristic may comprise one or more of, but is not limited to: decomposition methods, inductive methods, reduction methods, constructive methods, and/or local search methods.
 16. The building system controller of claim 1, wherein the heuristic may comprise a heuristic, metaheuristic, or hyperheuristic.
 17. The building system controller of claim 16, wherein the heuristic, metaheuristic, or hyperheuristic may be comprised of, but is not limited to, one or more of: particle swarm organization, self-organizing migration algorithm, neural networks, group method of data handling, differential evolution, genetic algorithm, memetic algorithm, random forest, hill climbing algorithm, simulated annealing, monte-carlo methods, random search, fuzzy-logic, arithmetic mean, geometric mean, harmonic mean, trimean, median, mode, mid-range, quadratic mean (RMS), cubic mean, generalized mean, weighted mean, linear regression, logistic regression, polynomial regression, k-means clustering, k-nearest neighbors, decision trees, perceptron, multi-layer perceptron (neural network), kernel methods, support vector machines, ensemble methods, boosting, bagging, naïve Bayes, expectation maximization, Gaussian mixture models, Gaussian processes, principal component analysis, singular value decomposition, reinforcement learning, Voronoi decomposition, or social theory voting techniques and concepts, such as social welfare functions, social choice functions, single transferrable vote, Bucklin's rule, social decision schemes, collective utility functions, and. Condorcet method and extensions such as Copeland's rule, maximin, Dodgson's rule, Young's rule, and ranked pairs.
 18. The building system controller of claim 1, wherein the data stream may be comprised of one or more of, but is not limited to: a data store, a real-time data feed.
 19. The building system controller of claim 1 wherein the data stream may comprise one or more of, but is not limited to: sensor data, actuation data, system parameters, equipment state, weather data, environmental data, occupant input, and/or occupant behavior.
 20. The building system controller of claim 1, wherein the training loop performs regression using the heuristic on the simulation engine output and the data stream to improve the physical model, by varying the model parameters or adapting the underlying structure and composition of the model.
 21. A electronic device or network of electronic devices comprising: a coupled building system; a data stream; at least one memory; and one or more processors operatively coupled to the data stream and one or more processors operatively coupled to the memories, where the processors are configured to execute program code stored in the memories to: control the coupled building system; receive sensor measurements obtained with the data stream, and store the data he memory; compute a mathematical model that substantially imitates the behavior of the controlled system, generating a predicted data stream measurement; analyze the variation between the stored data stream and the predicted data stream, using a cost function. iterate the process of computation and analysis, while changing parameters of the mathematical model as to regress the model in a manner that the output of the simulation engine approaches the data stream; control the coupled building system using predicted actuation using the regressed mathematical model:
 22. The electronic device of claim 21, wherein the mathematical model may be computed repeatedly, in arbitrary order, to evaluate each of an arbitrary number of different physical models.
 23. The electronic device of claim 21, wherein the mathematical model computation engine may comprise one or more computational entities in a network of simulation engines.
 24. The electronic device of claim 21, wherein the mathematical model computation engine may be comprised of collocated computational entities as well as remote computational entities. 